Synthetic Biology Blends Math, Computer Science, and Biology

A. Malcolm Campbell

Reed CollegeMarch 7, 2008

What is Synthetic Biology?

BioBrick Registry of Standard Parts

http://parts.mit.edu/registry/index.php/Main_Page

Peking University Imperial College

What is iGEM?

Davidson CollegeMalcolm Campbell (bio.)Laurie Heyer (math)Lance HardenSabriya Rosemond (HU)Samantha SimpsonErin Zwack

Missouri Western State U.Todd Eckdahl (bio.)Jeff Poet (math)Marian BroderickAdam BrownTrevor ButnerLane Heard (HS student)Eric JessenKelley MalloyBrad Ogden

SYNTHETIC BIOLOGYiGEM 2006

Enter: Flapjack & The Hotcakes

Erin Zwack (Jr. Bio); Lance Harden (Soph. Math); Sabriya Rosemond (Jr. Bio)

Erin Zwack (Jr. Bio); Lance Harden (Soph. Math); Sabriya Rosemond (Jr. Bio)

Enter: Flapjack & The Hotcakes

Wooly Mammoths of Missouri Western

1234

Burnt Pancake Problem

Burnt Pancake Problem

Burnt Pancake Problem

Look familiar?

How to Make Flippable DNA Pancakes

hixCRBSRBS

hixC

TetTet

hixCpBad

pancake 1 pancake 2

Flipping DNA with Hin/hixC

Flipping DNA with Hin/hixC

Flipping DNA with Hin/hixC

How to Make Flippable DNA Pancakes

All on 1 Plasmid: Two pancakes (Amp vector) + Hin

hixCRBSRBS

hixC

TetTet

hixCpBad

pancake 1 pancake 2

TT TT

pLac

RBSRBS Hin LVAHin LVA

Hin Flips DNA of Different Sizes

Hin Flips Individual Segments

-2 1

No Equilibrium 11 hrs Post-transformation

Hin Flips Paired Segments

white light u.v.

mRFP off

mRFP on double-pancake flip

-2 1

2-1

Modeling to Understand Flipping

( 1, 2)(-2, -1)

( 1, -2)(-1, 2)(-2, 1)( 2, -1)

(-1, -2)( 2, 1)

(1,2) (-1,2)

(1,-2) (-1,-2)

(-2,1)(-2,-1)

(2,-1) (2,1)

( 1, 2)(-2, -1)

( 1, -2)(-1, 2)(-2, 1)( 2, -1)

(-1, -2)( 2, 1)

(1,2) (-1,2)

(1,-2) (-1,-2)

(-2,1)(-2,-1)

(2,-1) (2,1)

1 flip: 0% solved

Modeling to Understand Flipping

( 1, 2)(-2, -1)

( 1, -2)(-1, 2)(-2, 1)( 2, -1)

(-1, -2)( 2, 1)

(1,2) (-1,2)

(1,-2) (-1,-2)

(-2,1)(-2,-1)

(2,-1) (2,1)

2 flips: 2/9 (22.2%)solved

Modeling to Understand Flipping

PRACTICAL

Proof-of-concept for bacterial computers

Data storagen units gives 2n(n!) combinations

BASIC BIOLOGY RESEARCH

Improved transgenes in vivo

Evolutionary insights

Consequences of DNA Flipping Devices -1,2 -2,-1

in 2 flips!

genegeneregulatorregulator

Success at iGEM 2006

Living Hardware to Solve the Hamiltonian Path Problem,

2007

Faculty: Malcolm Campbell, Todd Eckdahl, Karmella Haynes, Laurie Heyer, Jeff Poet

Students: Oyinade Adefuye,

Will DeLoache, Jim Dickson,

Andrew Martens, Amber Shoecraft, and Mike Waters; Jordan Baumgardner, Tom

Crowley, Lane Heard, Nick Morton, Michelle Ritter, Jessica Treece,

Matt Unzicker, Amanda Valencia

The Hamiltonian Path Problem

1

3

5

4

2

1

3

5

4

2

The Hamiltonian Path Problem

Advantages of Bacterial Computation

Software Hardware Computation

Computation

Computation

Advantages of Bacterial Computation

Software Hardware Computation

Computation

Computation

$

¢

Cell Division

• Non-Polynomial (NP)

• No Efficient Algorithms

# of

Pro

cess

ors

Advantages of Bacterial Computation

3

Using Hin/hixC to Solve the HPPUsing Hin/hixC to Solve the

HPP

1 54 34 23 41 42 53 14

1

3

5

4

2

3

Using Hin/hixC to Solve the HPP

1 54 34 23 41 42 53 14

hixC Sites

1

3

5

4

2

Using Hin/hixC to Solve the HPP

Using Hin/hixC to Solve the HPP

1

3

5

4

2

Using Hin/hixC to Solve the HPP

Using Hin/hixC to Solve the HPP

1

3

5

4

2

Using Hin/hixC to Solve the HPP

Using Hin/hixC to Solve the HPP

1

3

5

4

2

Using Hin/hixC to Solve the HPP

Solved Hamiltonian Path

1

3

5

4

2

Using Hin/hixC to Solve the HPP

How to Split a Gene

RBSRBS

Promoter

ReporterReporter

hixCRBSRBS

Promoter

Repo- Repo- rter

Detectable Phenotype

Detectable Phenotype

?

Gene Splitter Software

Input Output

1. Gene Sequence (cut and paste)

2. Where do you want your hixC site?

3. Pick an extra base to avoid a frameshift.

1. Generates 4 Primers (optimized for Tm).

2. Biobrick ends are added to primers.

3. Frameshift is eliminated.

http://gcat.davidson.edu/iGEM07/genesplitter.html

Gene-Splitter Output

Note: Oligos are optimized for Tm.

Predicting Outcomes of Bacterial Computation

Pro

babi

lity

of H

PP

Sol

utio

n

Number of Flips

4 Nodes & 3 Edges

Starting Arrangements

k = actual number of occurrences

λ = expected number of occurrences

λ = m plasmids * # solved permutations of edges ÷ # permutations of edges

Cumulative Poisson Distribution:

P(# of solutions ≥ k) =

€

1−e−λ • λ x

x!x=0

k−1

∑

k = 1 5 10 20

m = 10,000,000 .0697 0 0 0

50,000,000 .3032 .00004 0 0

100,000,000 .5145 .0009 0 0

200,000,000 .7643 .0161 .000003 0

500,000,000 .973 .2961 .0041 0

1,000,000,000 .9992 .8466 .1932 .00007

Probability of at least k solutions on m plasmids for a 14-edge graph

How Many Plasmids Do We Need?

False Positives

Extra Edge

1

3

5

4

2

PCR Fragment Length

PCR Fragment Length

1

3

5

4

2

False Positives

Detection of True Positives

0

0.25

0.5

0.75

1

4/6 6/9 7/12 7/14

# of

Tru

e P

ositi

ves

÷

Tot

al #

of P

ositi

ves

# of Nodes / # of Edges

1.00

10.00

100.00

1000.00

10000.00

100000.00

1000000.00

10000000.00

100000000.00

4/6 6/9 7/12 7/14

# of Nodes / # of Edges

Total # of Positives

# of Nodes / # of Edges

Tot

al #

of P

ositi

ves

How to Build a Bacterial Computer

Choosing Graphs

Graph 2

A

B

D

Graph 1

A

B

C

Splitting Reporter Genes

Green Fluorescent Protein Red Fluorescent Protein

GFP Split by hixC

Splitting Reporter Genes

RFP Split by hixC

HPP Constructs

Graph 1 Constructs:

Graph 2 Construct:

AB

ABC

ACB

BAC

DBA

Graph 0 Construct:

Graph 2

Graph 1

B

AC

A D

B

Graph 0

B

A

T7 RNAP

Hin + Unflipped

HPP

TransformationPCR to

Remove Hin

& Transform

Coupled Hin & HPP Graph

Flipping Detected by Phenotype

ACB

(Red)

BAC(None)

ABC(Yellow)

Hin-Mediated FlippingACB

(Red)

BAC(None)

ABC(Yellow)

Flipping Detected by Phenotype

ABC Flipping

Yellow

HinYellow, Green, Red, None

Red

ACB Flipping

HinYellow, Green, Red, None

BAC Flipping

Hin None

Yellow, Green, Red, None

Flipping Detected by PCR

ABC

ACB

BAC

Unflipped Flipped

ABCACB

BAC

Flipping Detected by PCR

ABC

ACB

BAC

Unflipped Flipped

ABCACB

BAC

RFP1 hixC GFP2

BAC

Flipping Detected by Sequencing

RFP1 hixC GFP2

RFP1 hixC RFP2

BAC

Flipped-BAC

Flipping Detected by Sequencing

Hin

Conclusions

• Modeling revealed feasibility of our approach

• GFP and RFP successfully split using hixC

• Added 69 parts to the Registry

• HPP problems given to bacteria

• Flipping shown by fluorescence, PCR, and sequence

• Bacterial computers are working on the HPP and may have solved it

Living Hardware to Solve the

Hamiltonian Path Problem

Acknowledgements: Thanks to The Duke Endowment, HHMI, NSF DMS 0733955, Genome Consortium for Active Teaching, Davidson College James G. Martin

Genomics Program, Missouri Western SGA, Foundation, and Summer Research Institute, and Karen Acker (DC ’07). Oyinade Adefuye is from North Carolina Central

University and Amber Shoecraft is from Johnson C. Smith University.

What is the Focus?

Thanks to my life-long collaborators

DNA Microarrays:windows into a functional

genome

Opportunities for Undergraduate Research

How do microarrays work?

How do microarrays work?

How do microarrays work?

How do microarrays work?

See Animation

Open Source and Free Software

www.bio.davidson.edu/MAGIC

How Can Microarrays be Introduced?

Ben Kittinger ‘05

Wet-lab microarray simulation kit - fast, cheap, works every time.

How Can Students Practice?

www.bio.davidson.edu/projects/GCAT/Spot_synthesizer/Spot_synthesizer.html

What Else Can Chips Do?

Jackie Ryan ‘05

Comparative Genome Hybridizations

Extra Slides

Can we build a biological computer?

The burnt pancake problem can be modeled as DNA

(-2, 4, -1, 3) (1, 2, 3, 4)

DNA Computer Movie >>

Design of controlled flipping

RBS-mRFP(reverse)

hix RBS-tetA(C)hixpLac hix